This paper presents work on vision based robotic grasping. The proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. We evaluate and compare the performance of linear and non-linear classifiers. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.

Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.

For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking
while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom
lightweight systems, conventional identification of rigid body dynamics models using CAD data and
actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method
is data-driven parameter estimation, but significant noise in measured and inferred variables affects it
adversely. Moreover, standard estimation procedures may give physically inconsistent results due to
unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing
a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor
Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm
that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and
output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems,
achieving an error of up to three times lower than other state-of-the-art machine learning methods.

In this work we present the ﬁrst constrained stochastic op-
timal feedback controller applied to a fully nonlinear, tendon
driven index ﬁnger model. Our model also takes into account an
extensor mechanism, and muscle force-length and force-velocity
properties. We show this feedback controller is robust to noise
and perturbations to the dynamics, while successfully handling
the nonlinearities and high dimensionality of the system. By ex-
tending prior methods, we are able to approximate physiological
realism by ensuring positivity of neural commands and tendon
tensions at all timesthus can, for the ﬁrst time, use the optimal control framework
to predict biologically plausible tendon tensions for a nonlinear
neuromuscular ﬁnger model.
METHODS
1 Muscle Model
The rigid-body triple pendulum ﬁnger model with slightly
viscous joints is actuated by Hill-type muscle models. Joint
torques are generated by the seven muscles of the index ﬁn-

We present a novel algorithm for efficient learning and feature selection in high-
dimensional regression problems. We arrive at this model through a modification of
the standard regression model, enabling us to derive a probabilistic version of the
well-known statistical regression technique of backfitting. Using the Expectation-
Maximization algorithm, along with variational approximation methods to overcome
intractability, we extend our algorithm to include automatic relevance detection
of the input features. This Variational Bayesian Least Squares (VBLS) approach
retains its simplicity as a linear model, but offers a novel statistically robust â??black-
boxâ? approach to generalized linear regression with high-dimensional inputs. It can
be easily extended to nonlinear regression and classification problems. In particular,
we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector
Machine, with VBLS at its core, offering significant computational and robustness
advantages for this class of methods. We evaluate our algorithm on synthetic and
neurophysiological data sets, as well as on standard regression and classification
benchmark data sets, comparing it with other competitive statistical approaches
and demonstrating its suitability as a drop-in replacement for other generalized
linear regression techniques.

In the proceedings of American Control Conference (ACC 2010) , 2010, clmc (article)

Abstract

We present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state- and control-dependent process noise, and proceed to derive the second-order expansion of the cost-to-go. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

In a not too distant future, robots will be a natural part of
daily life in human society, providing assistance in many
areas ranging from clinical applications, education and care
giving, to normal household environments [1]. It is hard to
imagine that all possible tasks can be preprogrammed in such
robots. Robots need to be able to learn, either by themselves
or with the help of human supervision. Additionally, wear and
tear on robots in daily use needs to be automatically compensated
for, which requires a form of continuous self-calibration,
another form of learning. Finally, robots need to react to stochastic
and dynamic environments, i.e., they need to learn
how to optimally adapt to uncertainty and unforeseen
changes. Robot learning is going to be a key ingredient for the
future of autonomous robots.
While robot learning covers a rather large field, from learning
to perceive, to plan, to make decisions, etc., we will focus
this review on topics of learning control, in particular, as it is
concerned with learning control in simulated or actual physical
robots. In general, learning control refers to the process of
acquiring a control strategy for a particular control system and
a particular task by trial and error. Learning control is usually
distinguished from adaptive control [2] in that the learning system
can have rather general optimization objectivesâ??not just,
e.g., minimal tracking errorâ??and is permitted to fail during
the process of learning, while adaptive control emphasizes fast
convergence without failure. Thus, learning control resembles
the way that humans and animals acquire new movement
strategies, while adaptive control is a special case of learning
control that fulfills stringent performance constraints, e.g., as
needed in life-critical systems like airplanes.
Learning control has been an active topic of research for at
least three decades. However, given the lack of working robots
that actually use learning components, more work needs to be
done before robot learning will make it beyond the laboratory
environment. This article will survey some ongoing and past
activities in robot learning to assess where the field stands and
where it is going. We will largely focus on nonwheeled robots
and less on topics of state estimation, as typically explored in
wheeled robots [3]â??6], and we emphasize learning in continuous
state-action spaces rather than discrete state-action spaces [7], [8].
We will illustrate the different topics of robot learning with
examples from our own research with anthropomorphic and
humanoid robots.

We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing
it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization, and control techniques
to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal
foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-
Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force
control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller
by testing it on the LittleDog quadruped robot, over a wide variety of rough terrains of varying difficulty levels. The
terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length
of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by
an independent external test team on terrain that has never been shown to us.

2008

One of the most general frameworks for phrasing control problems for
complex, redundant robots is operational space control. However, while
this framework is of essential importance for robotics and well-understood
from an analytical point of view, it can be prohibitively hard to achieve
accurate control in face of modeling errors, which are inevitable in com-
plex robots, e.g., humanoid robots. In this paper, we suggest a learning
approach for opertional space control as a direct inverse model learning
problem. A ï¬rst important insight for this paper is that a physically cor-
rect solution to the inverse problem with redundant degrees-of-freedom
does exist when learning of the inverse map is performed in a suitable
piecewise linear way. The second crucial component for our work is based
on the insight that many operational space controllers can be understood
in terms of a constrained optimal control problem. The cost function as-
sociated with this optimal control problem allows us to formulate a learn-
ing algorithm that automatically synthesizes a globally consistent desired
resolution of redundancy while learning the operational space controller.
From the machine learning point of view, this learning problem corre-
sponds to a reinforcement learning problem that maximizes an immediate
reward. We employ an expectation-maximization policy search algorithm
in order to solve this problem. Evaluations on a three degrees of freedom
robot arm are used to illustrate the suggested approach. The applica-
tion to a physically realistic simulator of the anthropomorphic SARCOS
Master arm demonstrates feasibility for complex high degree-of-freedom
robots. We also show that the proposed method works in the setting of
learning resolved motion rate control on real, physical Mitsubishi PA-10
medical robotics arm.

Dexterous manipulation with a highly redundant movement system is one of the hallmarks of hu-
man motor skills. From numerous behavioral studies, there is strong evidence that humans employ
compliant task space control, i.e., they focus control only on task variables while keeping redundant
degrees-of-freedom as compliant as possible. This strategy is robust towards unknown disturbances
and simultaneously safe for the operator and the environment. The theory of operational space con-
trol in robotics aims to achieve similar performance properties. However, despite various compelling
theoretical lines of research, advanced operational space control is hardly found in actual robotics imple-
mentations, in particular new kinds of robots like humanoids and service robots, which would strongly
profit from compliant dexterous manipulation. To analyze the pros and cons of different approaches
to operational space control, this paper focuses on a theoretical and empirical evaluation of different
methods that have been suggested in the literature, but also some new variants of operational space
controllers. We address formulations at the velocity, acceleration and force levels. First, we formulate
all controllers in a common notational framework, including quaternion-based orientation control, and
discuss some of their theoretical properties. Second, we present experimental comparisons of these
approaches on a seven-degree-of-freedom anthropomorphic robot arm with several benchmark tasks.
As an aside, we also introduce a novel parameter estimation algorithm for rigid body dynamics, which
ensures physical consistency, as this issue was crucial for our successful robot implementations. Our
extensive empirical results demonstrate that one of the simplified acceleration-based approaches can
be advantageous in terms of task performance, ease of parameter tuning, and general robustness and
compliance in face of inevitable modeling errors.

In this paper we introduce an improved implementation of locally weighted projection regression
(LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data.
As the key features, our code supports multi-threading, is available for multiple platforms, and
provides wrappers for several programming languages.

2003

Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358(1431):537-547, 2003, clmc (article)

Abstract

Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking - indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.

1994

This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real-time learning of the task within 40 to 100 trials.

1991

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems